# Langchain Langgraph > Use when working with LangChain or LangGraph in this repo, especially for agent construction, middleware, state graphs, persistence, memory, retrieval, runtime context, or durable execution patterns. - Skill: `tomevault-io/langchain-langgraph` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/langchain-langgraph` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/langchain-langgraph/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/langchain-langgraph --- # LangChain LangGraph Use this skill as the project-local dispatcher for LangChain and LangGraph guidance derived from the curated reference in `.github/LangChain-LangGraph_organized_reference.md`. ## Workflow 1. Open `references/index.md` first. 2. Pick the smallest matching reference file instead of loading everything. 3. Prefer the LangChain-side files for agent construction, prompts, tools, ToolRuntime, middleware, model routing, and structured output. 4. Prefer the LangGraph-side files for graph design, reducers, checkpoints, interrupts, subgraphs, streaming, and durable execution. 5. Prefer the memory and retrieval files for long-term memory, state/store separation, vector stores, loaders, splitters, embeddings, and RAG architecture. 6. Preserve the original source doc as the canonical reference for cross-checking; this skill is a bounded access layer, not a replacement source. ## High-Value Repo Rules - Prefer explicit LangGraph orchestration for complex workflows instead of burying a full agent loop inside a graph node. - Reuse model and agent instances instead of rebuilding them inside each call path. - Use structured output consistently for LLM and tool-facing boundaries when typed data matters. - Keep async usage end-to-end across LangChain and LangGraph integrations. - Preserve provider-specific message metadata when the model requires it across turns. - Normalize persisted agent state when loading from checkpointers if schema or version drift is possible. - Keep code examples close to the topic-specific references instead of collapsing them into abstract summaries. ## Reference Files - `references/index.md` - `references/01-langchain-overview.md` - `references/02-prompts-and-messages.md` - `references/03-tools-and-toolruntime.md` - `references/04-model-selection-and-structured-output.md` - `references/05-middleware-and-guardrails.md` - `references/06-runtime-state-store-context.md` - `references/07-langgraph-state-nodes-edges.md` - `references/08-checkpointing-persistence-durability.md` - `references/09-interrupts-hitl-resume.md` - `references/10-subgraphs-and-streaming.md` - `references/11-multi-agent-patterns.md` - `references/12-memory.md` - `references/13-retrieval-rag.md` ## Source Preservation The source reference remains in `.github/LangChain-LangGraph_organized_reference.md`. This skill is an opencode-native access layer and does not replace the preserved source document. --- > Source: [Harmeet10000/AgentNexus-LangChain-FastAPI](https://github.com/Harmeet10000/AgentNexus-LangChain-FastAPI) — distributed by [TomeVault](https://tomevault.io).